Real-Time Implementation of Model Predictive Multivariable Tracking Control for Hydrostatic Transmissions

Author(s):  
Dang Ngoc Danh ◽  
Harald Aschemann
Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.


Author(s):  
Ezz Eldin Ibrahim ◽  
Tarek Elnady ◽  
Mohamed Saffaa Hassan ◽  
Ibrahim Saleh

The presented work was directed to develop the dynamic performance of an electro-hydraulic proportional system (EHPS). A mathematical model of the EHPS is presented using electro- hydraulic proportional valve (EHPV) by Matlab-Simulink, which facilitates the simulation of the hydraulic behavior inside the main control unit. Experimental work is done and the closed loop system is designed using the linear variable displacement transducer sensor (LVDT). The controller of the system is an Arduino uno, which is considered as a processor of the system. The model is validated by the experimental system. The study also presents a real time tracking control method, based on pulse width modulation, by controlling the speed of the actuator to achieve the position tracking with minimum error and low transient time, by applying the constant input signal 50mm the transient time was 0.9 seconds and the error 1.8%.


2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.


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